%matplotlib inline
import pandas as pd
import numpy as np
import math
import scipy
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
Matplotlib¶
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
Question 1:¶
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
x = stocks['date']
y = stocks['GOOG']
fig, ax = plt.subplots(figsize=(15,9))
ax.plot(x,y)
ax.set_xticks(np.arange(0, len(x)+1, 14))
ax.set_title('Google stock')
ax.set_xlabel('date')
ax.set_ylabel('Stock value')
plt.show()
Question 2:¶
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
# YOUR CODE HERE
x = stocks['date']
y1 = stocks['GOOG']
y2 = stocks['AAPL']
y3 = stocks['AMZN']
y4 = stocks['FB']
y5 = stocks['NFLX']
y6 = stocks['MSFT']
fig, ax = plt.subplots(figsize=(15,9))
ax.plot(x,y1, linestyle='dashdot', marker='o')
ax.plot(x,y2, linestyle='solid', marker='p')
ax.plot(x,y3, linestyle='dotted', marker='v')
ax.plot(x,y4, linestyle='dashed', marker='<')
ax.plot(x,y5, marker='>')
ax.plot(x,y6, marker='s')
ax.set_xticks(np.arange(0, len(x)+1, 14))
ax.set_title('Stocks')
ax.set_xlabel('date')
ax.set_ylabel('Stock value')
plt.legend()
plt.show()
Seaborn¶
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
Question 3:¶
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
- Are there differences between male and female when it comes to giving tips?
- What attribute correlate the most with tip?
#Do most people tip at lunchtime or dinnertime?
g = sns.FacetGrid(tips, hue='time')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.show()
Plotly Express¶
Question 4:¶
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
The stocks dataset¶
Hints:
- Turn stocks dataframe into a structure that can be picked up easily with plotly express
# YOUR CODE HERE
fig = px.line(stocks, x="date", y = stocks.columns)
fig.show()
The tips dataset¶
#Do most people tip at lunchtime or dinnertime depending on the total bill?
fig2 = px.scatter(tips, x="total_bill", y = ["tip"], color="time")
fig2.show()
Question 5:¶
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
- Extract the 2007 year data from the dataframe. You have to process the data accordingly
- use plotly bar
- Add different colors for different continents
- Sort the order of the continent for the visualisation. Use axis layout setting
- Add text to each bar that represents the population
#load data
df = px.data.gapminder()
df.head()
# YOUR CODE HERE
df_2007 = df.query('year==2007')
a = df_2007.groupby('continent')
df_2007_new = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index , color =df_2007_new.index, orientation='h', text_auto = True)
fig.update_yaxes(categoryorder="min ascending")
fig.show()